Chemistry has provided many instances of test results that identify the presence or lack thereof of a chemical, and/or its concentration, by the color of a given solution or substance. This knowledge has resulted in many test processes that involve the combination of one or more substances to generate a chemical reaction that causes a change in color that can be seen by the human eye and interpreted for the result. A good example of this is the use of color test strips which comprise a small plastic strip with chemical impregnated patches glued thereto. The test strip may be dipped into a solution and, based on the color that the patches subsequently turn, an estimate may be made regarding the properties (for example pH, alkalinity and/or free chlorine levels of the solution).
The test strip is read by comparing the color of the patches on the test strip to a color chart that is often provided on the test strip container itself. Current test strip technology relies upon a human to dip the test strip in a consistent manner and to read the test strip within a prescribed time period. There is often variability in the test results that can be due to factors related to human error, including incorrect dip times, incorrect dipping technique, or incorrect time between dip and reading.
Additional variability and error is often introduced by the variation in the perception and interpretation of color between and amongst human test readers. Age, gender and individual acuity allow for a broad range of variability in determining color test results, which can result in reduced accuracy.
A color chart that can be used to correlate test colors to results is generally provided with currently available color tests. A user of the color test can compare the test strip color obtained from performing the test to the color chart in order to interpret the results of the test. However, the color chart is subject to printing process variability, inks used, paper used, exposure to sunlight and environmental elements. The resulting variability in test strip comparison charts creates additional variability in the human test reader's ability to accurately read the test strip. As a result of all of the variability described, color test strips are considered to be good, but not generally to be highly accurate at quantitative measurement.
In the areas of digital photography and digital imaging, it is known to employ software algorithms to adjust the color of the final image to an acceptable level for viewing. However, these algorithms are generally employed to provide an idealized look to the image, for example, to make the colors more appealing to users. These algorithms generally do not attempt to improve accuracy and/or precision of color comparisons between colors in either a film photograph or digital photograph and colors as they are perceived in real world conditions. Nor do they generally involve taking into account conditions existing at the time a photo is taken that could effect real world color measurements, or the effects that film processing or digital enhancement of colors can have in determining real world color values.
There remains a need for an improved method and system for more accurately determining color test results. There also remains a need for improved methods and systems for more accurately or precisely determining colors of objects as they are perceived in real world conditions using digital images or other color images of the objects.